i. Abstract

This document proposes a set of best practices and guidelines for implementing and using the Open Geospatial Consortium (OGC) Web Map Service (WMS) to serve maps which are members of an ensemble of maps, each of which is a valid possible alternative for the same time and location. In the meteorological and oceanographic communities, it is Best Practice to produce a large number of simultaneous forecasts, whether for a short range of hours, a few days, seasonal or climatological predictions. These ensembles of forecasts indicate the probability distributions of specific outcomes. This document describes how to unambiguously specify an individual member of an ensemble, or one of a limited set of map products derived from a full ensemble.

In particular, clarifications and restrictions on the use of WMS are defined to allow unambiguous and safe interoperability between clients and servers, in the context of expert meteorological and oceanographic usage and non-expert usage in other communities. This Best Practice document applies specifically to WMS version 1.3, but many of the concepts and recommendations will be applicable to other versions of WMS or to other OGC services, such as the Web Coverage Service.

ii.          Keywords

The following are keywords to be used by search engines and document catalogues:

meteorology, oceanography, ensemble, member, time, elevation, time-dependent, elevation-dependent, wms, web map service 1.3, 1.3.0, ogc, best practice, ogcdoc

iii.          Preface

This Best Practice document is the result of discussions within the Meteorology and Oceanography Domain Working Group (MetOcean DWG) of the Technical Committee (TC) of the Open Geospatial Consortium (OGC) regarding the use of the OGC Web Map Service (WMS) to provide map visualizations from the various types of data regularly produced, analyzed, and shared by those communities. The discussion considered the differences in the types of data as well as the issues, concerns, and responsibilities of data producers when sharing those data as maps with end users, including analysts within the meteorological and oceanographic communities, users with specific needs and the general public. The limited scope of the requirements and recommendations in this document reflects the consensus reached by groups with vastly different types of data, limitations in the current design of the WMS specification, and compromises to ensure these services remain applicable to a mass market audience. Future work includes extending this Best Practice once the community gains more experience with implementing the provisions of this document. This document does not require any changes to other OGC specifications, but it is hoped that the WMS specification will evolve to address issues encountered in this work such as providing a mechanism to define exclusive dimensions and to define sparse combinations of dimensions.

Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. The Open Geospatial Consortium shall not be held responsible for identifying any or all such patent rights.

Recipients of this document are requested to submit, with their comments, notification of any relevant patent claims or other intellectual property rights of which they may be aware that might be infringed by any implementation of the standard set forth in this document, and to provide supporting documentation when possible.

iv.          Submitting organizations

The following organizations submitted this Document to the Open Geospatial Consortium Inc.

• Deutscher Wetterdienst, Germany
• ECWMF
• KNMI, Ministry of Infrastructure and the Environment, Netherlands
• Météo-France
• Meteorological Service of Canada, Environment and Climate Change Canada
• UK Met Office
• US Air Force Directorate of Weather

v.          Submitters

All questions regarding this submission should be directed to the editors or the submitters:

1.             Introduction

The meteorological and oceanographic communities have been exchanging information internationally for at least 150 years and well understand the importance of geospatial standards for interoperability. These standards have typically defined data formats, interfaces, processes, shared conceptual models, and sustainable maintenance processes.

Because of the demanding nature of meteorological and oceanographic data processing, the communities have evolved domain specific solutions. However, as computers have become more powerful, it has become feasible to use general geospatial software for day-to-day operational purposes, and interoperability problems have arisen. There has also been an increasing need to combine meteorological and oceanographic data with other forms of geospatial data from other domains, in ways convenient for those domains.

Meteorological and oceanographic data are inherently multidimensional, not just in time and space but also over other dimensions, such as probability. In the meteorological and oceanographic communities, it is best practice to produce a number of simultaneous forecasts, whether for a short range of hours, a few days, a season or climatological predictions for a century. These ensembles of forecasts give an indication of the probability of specific outcomes.

This document describes and justifies a set of best practices for offering and requesting maps representing meteorological and oceanographic data selected from an ensemble of possibilities through WMS. This set of best practices is intended to meet the interoperability requirements of the meteorological and oceanographic communities and enable them and their customers to gain the economic benefits of using commercial, off the shelf, software implementations of WMS servers and clients.

1.1           Ensemble Forecast

Ensemble forecasts are the output of a numerical weather prediction system that facilitates the estimation of uncertainty in a weather forecast as well as the most likely outcome.

Instead of running the prediction once (a deterministic forecast), many predictions are computed, where each prediction uses slightly different input conditions. The result is called an ensemble forecast.

An ensemble forecast is a set of forecasts for the same times and locations. They are based on a set of equally likely scenarios, produced e.g. by perturbing the initial state, modifying the simulated physics, equation approximations, or boundary conditions. Any convergent or divergent distribution of the resulting set of forecasts can give an indication of the likelihood of the forecasts. Ensemble forecasts are not exact evolutions of a Probability Distribution Function for the atmosphere or oceans, as calculating these is currently an intractable problem.

When more ensemble forecasts are made, rather than fewer, the ensemble of possible outcomes is more likely to capture the most likely and the most extreme possibilities.

Generally, ensembles of about 10 forecasts are not enough, but 100 forecasts are more than ample to capture a practical range of possible outcomes.

There is also real value in combining ensembles, for the same times and locations, from different forecasting organizations, to produce a larger, multi-sourced ensemble which has improved skill compared to smaller, single-sourced, ensembles or even a similarly sized, single-sourced ensemble.

The production system is usually known as an EPS, Ensemble Prediction System.

1.2           Ensemble Member

The individual forecasts that comprise an ensemble are referred to as ensemble members. A forecasting service may select one member of an ensemble as the most appropriate prediction to offer to a customer (see Figure 1). Such a selection may be automatic or manual. A different organization’s ensemble may even be used, for example, as a back-up. Consequently, there is a need to identify a complete ensemble, a specific member, and the source or sources of that ensemble.

Contrast:

As all the ensemble members are, a priori, equally likely, there is no simple, easy to calculate, concept of two members being ‘near’ or ‘far’ from each other, or any one being the ‘most likely’.

1.3           Ensemble Product

This section describes the most common ensemble products and it briefly explains how they may be used. In general, two different types of ensemble products can be distinguished. One type delivers a chart that visualizes the data of all members. In the following, this type is called an all-member map. The other type produces new data as the result of a production process which takes all members as input. Some examples of this product type are aggregation maps, quantile maps or probability maps.

1.3.1      All-member maps

So-called postage stamp maps and spaghetti maps are the two most common ways to give an overview of all members.

A postage stamp map is a set of small maps showing plots of each individual ensemble member (see Figure 1). This allows the forecaster to view the scenarios in each member forecast and assess the possible risks of extreme events. However, this presents a large amount of information that can be difficult to comprehend.

A spaghetti map is a chart showing the contours of one or more variables from all ensemble members. This can provide a useful image of the predictability of the field. Where all ensemble member contours lie close together the predictability is higher; where they look like spaghetti on a plate, there is less predictability.

Consider for example Figure 2 and Figure 3. The graph in Figure 2 shows a 10-day temperature forecast for Brussels. There is confidence that it will become warmer for 4 or 5 days, and then probably cool, but the amount of cooling is less certain.

Figure 3 below shows a ‘spaghetti map’ of a North Atlantic, four day forecast of the ‘thickness’ of the lower atmosphere. Thickness is a measure of how warm or cold a layer of the atmosphere is. Usually a layer in the lower troposphere is chosen, between pressures of 1000 hPa and 500 hPa. The thickness is the difference in the heights of these two pressure levels, usually measured in decameters (Dm). A thicker layer is warmer than a ‘thinner’ layer. Thus, thickness acts as a proxy for the average temperature of the layer of atmosphere.

For example, a 1000-500hPa thickness of 528 Dm is relatively cold and indicative of snow rather than rain at sea level in Western Europe. The ensemble members, shown in the map of Figure 3, all consistently forecast this. But the forecasts of the warmer areas, indicated by a thickness of 564 Dm, are less certain.

Source:  UK Met Office using data from ECMWF, © UK Crown Copyright

Trajectory data present another example of meteorological data that often have multiple possibilities. A trajectory is the path that a moving object follows through space as a function of time. Trajectories are well recognized as often being very sensitive to the starting conditions, thus producing an ensemble of possible tracks is eminently sensible.

The distribution of possible trajectories can be shown by displaying all of them, or perhaps the extremes cases and an ‘average’ or ‘most likely’ track, though objectively defining what these are is a research topic and dependent on the detailed use case (see [Cheung 2014]).

Trajectories can run forward or backward. Good examples of forward trajectories are those for volcanic ash. They are usually calculated using the data of a numerical weather forecast. Such a forward trajectory predicts the movement of air masses from a given geographical position, in this case the location of the volcano. The trajectory has the same temporal and probabilistic associations as the numerical weather forecast because it is based on these data. An example of a backward trajectory is to find the upwind source of a nuclear pollution observation.

Figure 4 below shows two ensembles of forecasts for the tracks of two hurricanes, not unlike trajectories. A particular track could be chosen as the most likely. However, an ‘envelope’ of all possible forecast tracks could be constructed to be displayed with the most likely track, as in Figure 5.